The use of advanced machine vision systems and their underlying software is increasingly employed in a variety of manufacturing and quality control processes. Machine vision enables quicker, more accurate and repeatable results to be obtained in the production of both mass-produced and custom products. Basic machine vision systems include one or more cameras (typically having solid-state charge couple device (CCD) imaging elements) directed at an area of interest, frame grabber/image processing elements that capture and transmit CCD images, a computer and display for running the machine vision software application and manipulating the captured images, and appropriate illumination on the area of interest.
Many applications of machine vision involve the inspection of components and surfaces for defects that affect quality. Where sufficiently serious defects are noted, a part of a surface is marked as unacceptable/defective. Machine vision has also been employed in varying degrees to assist in manipulating manufacturing engines in the performance of specific tasks. Specifically, machine vision systems may be utilized for inspection of components along an assembly line to ensure that the components meet a predefined criteria before insertion and/or assembling of the components into a finished product.
Machine vision systems are typically utilized in alignment and inspection of components having a ball grid array (BGA) and/or flip chip form factor. BGA/flip chip components typically include a plurality of small solder balls on a mounting side of the component. The solder balls may then be soldered using ultrasound technology once a component is appropriately placed on a circuit board. Over the past few years, the number of balls on a flip chip have dramatically increased so that current flip chip components may have on the order of 12,000 balls. Furthermore, modern flip chip components typically have the solder balls less aligned on a grid pattern, i.e., the solder balls are non-uniformly spaced on the component.
Both of these trends complicate current machine vision systems that are utilized for alignment of flip chip designs. As the number of balls grows very large, current methods that rely on extracting balls or otherwise measuring ball features typically execute at a speed that is insufficient for run time. Furthermore, as the patterns of balls become more complex, search-based approach to alignments may enter worst-case scenarios. This may occur because a small misalignment in the translation or the angle may mean that a majority of individual features match thereby increasing the probability of an incorrect match occurring. Furthermore, flip chips often have strong body features present in an image obtained of the component. The body feature is typically not precisely aligned with the solder ball pattern, which means that these features must not be used for alignment. However existing machine vision tools are likely to use the strong body feature for alignment rather than the ball features, thereby resulting in unsatisfactory accuracy of alignment.
Additionally, conventional machine vision systems utilized for flip chips typically require geometric descriptions. However, a noted disadvantage since of such geometric descriptions is that they are extremely slow to train the geometric description when a flip chip has a non-repetitive pattern and/or a very large number of balls. As noted above, current trends in flip chip designs are increasing the number of balls and moving to non-repetitive, that is non-grid like patterns. As such, conventional machine vision systems for alignment of flip chips are becoming progressively slower as the current trends in design of flip chips continue.